# Ridge Regression for Addressing of the Multicollinearity Problem with Application in Cost of Production

• Ali Sadig Mohommed BAGER The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Iraq, Muthanna University
• Bahr KADHIM MOHAMMED The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Iraq, University of AL-Qadisiyah
• Meshal HARBI ODAH The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Iraq, Muthanna University
Keywords: Ridge regression, Mullticollinearity problem, Production of cement

### Abstract

The regression analysis is statistical method of extensive use, which illustrates the relationship between the explanatory variables and the dependent variable in the form of a model useful in the interpretation of scientific phenomenon, bringing also benefits to society. In this paper we study the most important factors affecting the cost production of cement (Muthanna Factory) by using the ridge regression. The factors are described as follows: we consider the cost of production amount as response variable and factors that affect or may affect the explanatory variables are labor, Price per ton, Electric power, Quantity consumed. They all suffer from high correlation, indicating a problem of multicollinearity .The data analysis is included in the study of the ridge regression as the best approach in case of a multicollinearity problem in the context of financial and economic data being associated with each other often. We used R packages (MASS).

### References

 Al-Jubouri & Habib C. "Multiple Regression and Analysis of Differentiation", (translated), Iraq: Higher Education Printing Press. 1990
 Dorugade, A.V. New ridge parameters for ridge regression. Journal of the Association of Arab Universities for Basic and Applied Sciences. 2014 (15). pp. 94-99
 Anwar F. & Lee Ceng Y. "Performance of Ridge Regression Estimator Method on Small Sample size By Varying correlation coefficients: A simulation study", Journal of Mathematics and statistics 10 (1). 2014. pp. 25 – 29
 García C.B., García, J., López Martín, M.M., & Salmerón, R.. Collinearity: Revisiting the variance inflation factor in ridge regression. Journal of Applied Statistics, 42(3), 2015. pp. 648-661
 Hoer, A.E, & Kennard R.W. Ridge Regression. Advances, Algorithms and Applications 1981: American Sciences Press. 1980
 Hoerl A.E., Kannard R.W., & Baldwin K.F. Ridge regression: some esimulations. Communications in Statistics-Theory and Methods. 4 (2). 1975. pp. 105-123
 Hoerl A.E., & R W. Kennard. "Ridge regression: Iterative estimation of the biasing parameter," Commun. Statist.A5. 1976. pp. 77-88
 Kibria, B.G. Performance of some new ridge regression estimators. Communications in Statistics-Simulation and Computation. 2003. 32 (2). pp. 419-435
 Kraha A., Turner H., Nimon K., Zientek L. R., & Henson R. K. Tools to support interpreting multiple regression in the face of multicollinearity. Frontiers in psychology. 3. 2012
 Kazem, Armory H. & Shalibah, M.B. "Advanced Economic Measurement Theory and Practice", Baghdad: Dunia al-Amal Library. 2002
 McDonald G.C. Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1). pp. 93-100. 2009
Published
2018-08-13
How to Cite
BAGER, A. S. M., KADHIM MOHAMMED, B., & HARBI ODAH, M. (2018). Ridge Regression for Addressing of the Multicollinearity Problem with Application in Cost of Production. LUMEN Proceedings, 3, 56-63. https://doi.org/10.18662/lumproc.nashs2017.4
Section
Articles